Take a step back.
Make informed decisions.
Ten practical perspectives on data, AI and governance. Reviewed in September 2026, with links to sources.
Microsoft Fabric: start with the use case, then choose the architecture
An integrated platform can simplify teamwork. It does not replace governance or an assessment of actual operating costs.
Insight · September 2026Quebec’s Law 25: build privacy into your data project
For businesses subject to Quebec privacy law, protecting personal information starts when the project is scoped.
Insight · September 2026LLMOps: making a generative AI service operational
Production readiness requires evaluations, identifiable versions and a planned response to incidents.
Insight · September 2026Data Mesh and Lakehouse: two different questions
One organizes responsibility for data. The other describes a platform approach. They can work together.
Insight · September 2026The EU AI Act: what Canadian organizations should check
Head-office location alone does not determine scope. Uses, markets and the organization’s role also matter.
Insight · September 2026Public-sector data governance: make responsibilities concrete
A catalogue is useful when teams know who decides, who fixes issues and which data can be shared.
Insight · September 2026Enterprise AI agents: choose controlled autonomy
A useful agent completes a defined task with proportionate permissions and a verifiable result.
Insight · September 2026Cloud in Canada: distinguish data residency from sovereignty
Hosting region matters. On its own, it does not describe every access path or dependency.
Insight · September 2026Microsoft 365 Copilot: govern access before expanding use
Start with the quality of permissions and content already present in your Microsoft 365 environment.
Insight · September 2026Real-time data: choose the right latency and plan for recovery
Kafka, Flink and Lakeflow pipelines play different roles. Start with the delay that actually matters to the business.
